Referanslar:
tr_annotated
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:xed_en_fi/en_annotated')
- Tanım :
A multilingual fine-grained emotion dataset. The dataset consists of human annotated Finnish (25k) and English sentences (30k). Plutchik’s
core emotions are used to annotate the dataset with the addition of neutral to create a multilabel multiclass
dataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to
show that XED performs on par with other similar datasets and is therefore a useful tool for
sentiment analysis and emotion detection.
- Lisans : Lisans: Creative Commons Atıf 4.0 Uluslararası Lisansı (CC-BY)
- Sürüm : 1.1.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'train' | 17528 |
- Özellikler :
{
"sentence": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"labels": {
"feature": {
"num_classes": 9,
"names": [
"neutral",
"anger",
"anticipation",
"disgust",
"fear",
"joy",
"sadness",
"surprise",
"trust"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}
tr_nötr
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:xed_en_fi/en_neutral')
- Tanım :
A multilingual fine-grained emotion dataset. The dataset consists of human annotated Finnish (25k) and English sentences (30k). Plutchik’s
core emotions are used to annotate the dataset with the addition of neutral to create a multilabel multiclass
dataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to
show that XED performs on par with other similar datasets and is therefore a useful tool for
sentiment analysis and emotion detection.
- Lisans : Lisans: Creative Commons Atıf 4.0 Uluslararası Lisansı (CC-BY)
- Sürüm : 1.1.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'train' | 9675 |
- Özellikler :
{
"sentence": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"labels": {
"num_classes": 9,
"names": [
"neutral",
"anger",
"anticipation",
"disgust",
"fear",
"joy",
"sadness",
"surprise",
"trust"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
}
fi_annotated
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:xed_en_fi/fi_annotated')
- Tanım :
A multilingual fine-grained emotion dataset. The dataset consists of human annotated Finnish (25k) and English sentences (30k). Plutchik’s
core emotions are used to annotate the dataset with the addition of neutral to create a multilabel multiclass
dataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to
show that XED performs on par with other similar datasets and is therefore a useful tool for
sentiment analysis and emotion detection.
- Lisans : Lisans: Creative Commons Atıf 4.0 Uluslararası Lisansı (CC-BY)
- Sürüm : 1.1.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'train' | 14449 |
- Özellikler :
{
"sentence": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"labels": {
"feature": {
"num_classes": 9,
"names": [
"neutral",
"anger",
"anticipation",
"disgust",
"fear",
"joy",
"sadness",
"surprise",
"trust"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}
fi_nötr
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:xed_en_fi/fi_neutral')
- Tanım :
A multilingual fine-grained emotion dataset. The dataset consists of human annotated Finnish (25k) and English sentences (30k). Plutchik’s
core emotions are used to annotate the dataset with the addition of neutral to create a multilabel multiclass
dataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to
show that XED performs on par with other similar datasets and is therefore a useful tool for
sentiment analysis and emotion detection.
- Lisans : Lisans: Creative Commons Atıf 4.0 Uluslararası Lisansı (CC-BY)
- Sürüm : 1.1.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'train' | 10794 |
- Özellikler :
{
"sentence": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"labels": {
"num_classes": 9,
"names": [
"neutral",
"anger",
"anticipation",
"disgust",
"fear",
"joy",
"sadness",
"surprise",
"trust"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
}